Determination of Virtual Surgery Mass Point Spring Model Parameters Based on Genetic Algorithms_Journal of Biomedical Engineering

Authors：

CHENYing ¹ , HUXuyi ¹ ,  ZHUQiguang ²

1. Key Laboratory of Test/Measurment Technology and Instrument of Hebei Province, Yanshan University, Qinhuangdao 066004, China;
2. College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

ZHUQiguang, Email: zhu7880@ysu.edu.cn

Keywords：

virtual reality; soft tissue deformation; mass point spring model; genetic algorithm

DOI：

10.7507/1001-5515.20150213

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Mass point-spring model is one of the commonly used models in virtual surgery. However, its model parameters have no clear physical meaning, and it is hard to set the parameter conveniently. We, therefore, proposed a method based on genetic algorithm to determine the mass-spring model parameters. Computer-aided tomography (CAT) data were used to determine the mass value of the particle, and stiffness and damping coefficient were obtained by genetic algorithm. We used the difference between the reference deformation and virtual deformation as the fitness function to get the approximate optimal solution of the model parameters. Experimental results showed that this method could obtain an approximate optimal solution of spring parameters with lower cost, and could accurately reproduce the effect of the actual deformation model as well.

Citation： CHENYing, HUXuyi, ZHUQiguang. Determination of Virtual Surgery Mass Point Spring Model Parameters Based on Genetic Algorithms. Journal of Biomedical Engineering, 2015, 32(6): 1202-1206. doi: 10.7507/1001-5515.20150213 Copy

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1. BEYER L, TRUYER J D, MANCINI J, et al. Impact of laparoscopy simulator training on the technical skills of future surgeons in the operating room:a prospective study[J]. The American Journal of Surgery, 2011, 202(3):265-272.
2. LOUKAS C, NIKITEAS N. Deconstructing laparoscopic competence in a virtual reality simulation environment[J]. Surgery, 2011, 149(6):750-760.
3. NIESEN M S C. Real-time volumetric deformable models for surgery simulation using finite elements and condensation[J]. Computer Graphics Forum, 1996, 15(3):57-66.
4. 徐少平, 刘小平, 张华, 等.虚拟手术中软组织实时形变模型的研究进展[J].生物医学工程学杂志, 2010, 27(2):435-439.
5. 刘文霞, 王树杰, 张继伟, 等.虚拟现实技术在医学上的应用[J].生物医学工程学杂志, 2007, 24(4):946-949.
6. DEUSSEN O, KOBBELT L, TUCKE P. Using simulated annealing to obtain good nodal approximations of deformable objects[C]//Computer Animation and Simulation'95. Maastricht:1995:30-43.
7. GELDER A V. Approximate simulation of elastic membranes by triangulated spring meshes[J]. Graph, 1998, 3(2):21-42.
8. MOLLEMANS W, SCHUTYSER F, CLEYNENBREUGELJ V. Tetrahedral mass spring model for fast soft tissue deformation[C]//Surgery Simulation and Soft Tissue Modeling. Juan-Les-Pins:2003:145-154.
9. COTIN S, DELINGETTE H, AYACHE N. A hybrid elastic model for real-time cutting deformations and force feedback for surgery training and simulation[J]. The Visual Computer, 2000, 16(7):437-452.
10. NURNBERGER A. Approximation of dynamic systems using recurrent neuro fuzzy techniques[J]. Soft Computing, 2004, 8(6):428-442.
11. BIANCHI G, SOLERTHALER B, SZEKELY G, et al. Simultaneous topology and stiffness identification for mass-spring models based on fem reference deformations[J]. Lecture Notes in Computer Science, 2004, 3217:293-301.
12. LOUCHET J, PROVET X, CROCHEMORE D. Evolutionary identification of cloth animation models[C]//Computer Animation and Simulation'95. Maastricht:1995:44-54.
13. IKER A, BASIL F. Filtering of high modal frequencies for stable real-time explicit integration of deformable objects using the Finite Element Method[J]. Progress in Biophysics and Molecular Biology, 2010, 103:225-235.
14. CHRIS GUY D F. An introduction to the principles of medical imaging[M]. London:Imperial College Press, 2005:95-98.
15. SUN S H, MILAN S. Lung segmentation refinement based on optimal surface finding utilizing a hybrid desktop/virtual reality user interface[J]. Computerized Medical Imaging and Graphics, 2013, 37:15-27.
16. LAM C K, SUNDARAJ K. Virtual simulation of eyeball and extra ocular muscle reaction during cataract surgery[J]. Procedia Engineering, 2012, 41:150-155.
17. 黎钧琪, 石国桢.遗传算法交叉率与变异率关系的研究[J].武汉理工大学学报, 2003, 27(1):97-99.

Journal of Biomedical Engineering

Determination of Virtual Surgery Mass Point Spring Model Parameters Based on Genetic Algorithms

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